Papers with Code 아카이브
Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 31
- 보존된 평가 기록
- 235
Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.
A common benchmark for this task is MPII Human Pose
<span style="color:grey; opacity: 0.6">( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN
https://github.com/teopb/ode-net-examples
https://github.com/ternaus/angiodysplasia-segmentation
Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning
https://github.com/tetrachrome/subpixel
https://github.com/thanhtrung98/Pose_estimation
https://github.com/thanhtrung98/human_pose_estimation
https://github.com/theerapatkitti/hand_mask_rcnn
https://github.com/theone1984/hand-gesture-ai
https://github.com/theyellowdiary/openpose
https://github.com/thomasslloyd/FitSpatial
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/thoughtmachines/Human-Pose-Estimation-using-HRNets
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/thstkdgus35/EDSR-PyTorch
https://github.com/thuml/ContextWM
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose
https://github.com/thuml/Transfer-Learning-Library/tree/dev-tllib/examples/domain_adaptation/keypoint_detection
Regressive Domain Adaptation for Unsupervised Keypoint Detection
https://github.com/thuxyz19/test
3D Human Pose Estimation with Spatial and Temporal Transformers
https://github.com/tiagoCuervo/JapaNet
https://github.com/tianfr/mononerf
https://github.com/tianyuan168326/videosemanticcompression-pytorch
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
https://github.com/tiev-tongji/quadric_slam
https://github.com/tinnunculus/Denoising_SR
https://github.com/tinymilky/RSANet
https://github.com/titu1994/Image-Super-Resolution
https://github.com/titu1994/tfdiffeq
https://github.com/tlpss/keypoint-detection
Learning Keypoints from Synthetic Data for Robotic Cloth Folding
https://github.com/tolgabirdal/qecnetworks
https://github.com/tom-doerr/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/tom-doerr/TecoGAN-Docker
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/tomas-gajarsky/facetorch
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
https://github.com/tonysy/PyAction
https://github.com/totoroKalic/pamtri-mindspore
https://github.com/toytiny/radarocc
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
https://github.com/tqchen/xgboost
https://github.com/transpchan/Live3D-v2
https://github.com/tryolabs/luminoth
https://github.com/tteepe/CenterNet-pytorch-lightning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.